{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d91ea6c-9f4c-4c9e-9e33-f4333482eee5",
   "metadata": {},
   "source": [
    "# Max Independent Set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877ae3cf-10cc-4e51-a428-eb57687d8d18",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In the Maximum Independent Set Problem [[1](#MISWiki)], we need to find the largest subset of vertices in a given graph, such that no two vertices in the subset are adjacent. This is an NP-Hard problem in general graph structures, with applications in various fields such as network deign, bioinformatics, and scheduling.\n",
    "\n",
    "## Mathematical Formulation\n",
    "Given a graph $G=(V,E)$, an independent set $I \\subseteq V$ is a set of vertices such that no two vertices in $I$ are adjacent. The Maximum Independent Set Problem is the problem of finding the independent set $I$ with maximum cardinality. In binary form, we can represent each vertex $v$ being in or out of the independent set $I$ by a binary variable $x_v$, with $x_v = 1$ if $v \\in I$, and $x_v = 0$ otherwise. The problem can then be formulated as:\n",
    "\n",
    "Maximize $\\sum_{v \\in V} x_v$\n",
    "\n",
    "Subject to:\n",
    "\n",
    "$x_{u} + x_{v} \\leq 1, \\forall (u, v) \\in E$\n",
    "\n",
    "where each $x_v \\in {0,1}$.\n",
    "\n",
    "\n",
    "# Solving with the Classiq platform\n",
    "\n",
    "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:12.856430Z",
     "iopub.status.busy": "2024-05-07T15:52:12.856181Z",
     "iopub.status.idle": "2024-05-07T15:52:13.399271Z",
     "shell.execute_reply": "2024-05-07T15:52:13.398641Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import cast\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "from IPython.display import Markdown, display\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8517ef73-faf6-4fd8-9db8-4088551398e0",
   "metadata": {},
   "source": [
    "## Building the Pyomo model from a graph input\n",
    "\n",
    "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48889b21-557b-481c-80c5-3c0b5c91adb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:13.402476Z",
     "iopub.status.busy": "2024-05-07T15:52:13.401869Z",
     "iopub.status.idle": "2024-05-07T15:52:13.406304Z",
     "shell.execute_reply": "2024-05-07T15:52:13.405679Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pyomo.core as pyo\n",
    "\n",
    "\n",
    "def mis(graph: nx.Graph) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel()\n",
    "    model.x = pyo.Var(graph.nodes, domain=pyo.Binary)\n",
    "\n",
    "    @model.Constraint(graph.edges)\n",
    "    def independent_rule(model, node1, node2):\n",
    "        return model.x[node1] + model.x[node2] <= 1\n",
    "\n",
    "    model.cost = pyo.Objective(expr=sum(model.x.values()), sense=pyo.maximize)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44cfaba-0269-4b77-a208-cab1a810b1d7",
   "metadata": {},
   "source": [
    "The model consists of:\n",
    "\n",
    "- Index set declarations (model.Nodes, model.Arcs).\n",
    "- Binary variable declaration for each node (model.x) indicating whether that node is chosen to be included in the set.\n",
    "- Constraint rule - for each edge we require at least one of the corresponding node variables to be 0.\n",
    "- Objective rule \u2013 the sum of the variables equals to the set size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "439b4081-cb00-4a59-bc23-20a21a291f67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:13.408755Z",
     "iopub.status.busy": "2024-05-07T15:52:13.408266Z",
     "iopub.status.idle": "2024-05-07T15:52:14.007366Z",
     "shell.execute_reply": "2024-05-07T15:52:14.006681Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "num_nodes = 8\n",
    "p_edge = 0.4\n",
    "graph = nx.fast_gnp_random_graph(n=num_nodes, p=p_edge, seed=12345)\n",
    "\n",
    "nx.draw_kamada_kawai(graph, with_labels=True)\n",
    "mis_model = mis(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "345330b2-9c14-41f6-b4ba-e11fb9ca1565",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:14.012596Z",
     "iopub.status.busy": "2024-05-07T15:52:14.011279Z",
     "iopub.status.idle": "2024-05-07T15:52:14.020876Z",
     "shell.execute_reply": "2024-05-07T15:52:14.020169Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Set Declarations\n",
      "    independent_rule_index : Size=1, Index=None, Ordered=False\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     2 :    Any :   14 : {(0, 2), (0, 4), (0, 6), (0, 7), (1, 2), (1, 4), (1, 5), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6), (5, 6)}\n",
      "    x_index : Size=1, Index=None, Ordered=False\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :    8 : {0, 1, 2, 3, 4, 5, 6, 7}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=8, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :  None :     1 : False :  True : Binary\n",
      "          1 :     0 :  None :     1 : False :  True : Binary\n",
      "          2 :     0 :  None :     1 : False :  True : Binary\n",
      "          3 :     0 :  None :     1 : False :  True : Binary\n",
      "          4 :     0 :  None :     1 : False :  True : Binary\n",
      "          5 :     0 :  None :     1 : False :  True : Binary\n",
      "          6 :     0 :  None :     1 : False :  True : Binary\n",
      "          7 :     0 :  None :     1 : False :  True : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : maximize : x[0] + x[1] + x[2] + x[3] + x[4] + x[5] + x[6] + x[7]\n",
      "\n",
      "1 Constraint Declarations\n",
      "    independent_rule : Size=14, Index=independent_rule_index, Active=True\n",
      "        Key    : Lower : Body        : Upper : Active\n",
      "        (0, 2) :  -Inf : x[0] + x[2] :   1.0 :   True\n",
      "        (0, 4) :  -Inf : x[0] + x[4] :   1.0 :   True\n",
      "        (0, 6) :  -Inf : x[0] + x[6] :   1.0 :   True\n",
      "        (0, 7) :  -Inf : x[0] + x[7] :   1.0 :   True\n",
      "        (1, 2) :  -Inf : x[1] + x[2] :   1.0 :   True\n",
      "        (1, 4) :  -Inf : x[1] + x[4] :   1.0 :   True\n",
      "        (1, 5) :  -Inf : x[1] + x[5] :   1.0 :   True\n",
      "        (2, 4) :  -Inf : x[2] + x[4] :   1.0 :   True\n",
      "        (2, 5) :  -Inf : x[2] + x[5] :   1.0 :   True\n",
      "        (2, 6) :  -Inf : x[2] + x[6] :   1.0 :   True\n",
      "        (3, 4) :  -Inf : x[3] + x[4] :   1.0 :   True\n",
      "        (3, 5) :  -Inf : x[3] + x[5] :   1.0 :   True\n",
      "        (3, 6) :  -Inf : x[3] + x[6] :   1.0 :   True\n",
      "        (5, 6) :  -Inf : x[5] + x[6] :   1.0 :   True\n",
      "\n",
      "5 Declarations: x_index x independent_rule_index independent_rule cost\n"
     ]
    }
   ],
   "source": [
    "mis_model.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "816b468f-a59f-4f2f-8337-4a9d66548425",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:14.025670Z",
     "iopub.status.busy": "2024-05-07T15:52:14.024439Z",
     "iopub.status.idle": "2024-05-07T15:52:16.270258Z",
     "shell.execute_reply": "2024-05-07T15:52:16.269600Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:16.273418Z",
     "iopub.status.busy": "2024-05-07T15:52:16.272601Z",
     "iopub.status.idle": "2024-05-07T15:52:16.276365Z",
     "shell.execute_reply": "2024-05-07T15:52:16.275788Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:16.278670Z",
     "iopub.status.busy": "2024-05-07T15:52:16.278371Z",
     "iopub.status.idle": "2024-05-07T15:52:16.422355Z",
     "shell.execute_reply": "2024-05-07T15:52:16.421734Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=mis_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcc3812-c9d0-421c-84bb-38047297b33f",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53bc041f-065c-44d2-b220-dafd9d0504ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:16.425237Z",
     "iopub.status.busy": "2024-05-07T15:52:16.424825Z",
     "iopub.status.idle": "2024-05-07T15:52:16.435948Z",
     "shell.execute_reply": "2024-05-07T15:52:16.435280Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a754e2c-a9e7-40c4-8cee-a9a3f5a86896",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:16.438408Z",
     "iopub.status.busy": "2024-05-07T15:52:16.438033Z",
     "iopub.status.idle": "2024-05-07T15:52:16.452664Z",
     "shell.execute_reply": "2024-05-07T15:52:16.452057Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"max_independent_set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:16.455142Z",
     "iopub.status.busy": "2024-05-07T15:52:16.454684Z",
     "iopub.status.idle": "2024-05-07T15:52:19.876612Z",
     "shell.execute_reply": "2024-05-07T15:52:19.875932Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/7e58acc3-dfdb-4ebf-88c6-c980aa6e5089?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:19.879287Z",
     "iopub.status.busy": "2024-05-07T15:52:19.878734Z",
     "iopub.status.idle": "2024-05-07T15:52:25.320726Z",
     "shell.execute_reply": "2024-05-07T15:52:25.319931Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02454398-b229-403c-824a-b1eb539fbc1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.325641Z",
     "iopub.status.busy": "2024-05-07T15:52:25.324432Z",
     "iopub.status.idle": "2024-05-07T15:52:25.356784Z",
     "shell.execute_reply": "2024-05-07T15:52:25.356169Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615ed612-b835-4bf0-aa92-92d30ef8006d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670eddd3-2da7-4a88-b571-7884ef24f60c",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "516d78ba-2951-46eb-b1af-efe877513556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.360585Z",
     "iopub.status.busy": "2024-05-07T15:52:25.360332Z",
     "iopub.status.idle": "2024-05-07T15:52:25.445578Z",
     "shell.execute_reply": "2024-05-07T15:52:25.444791Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 1, 1]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.016</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 0, 1]</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.023</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 1, 1]</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.035</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[0, 0, 1, 1, 0, 0, 0, 1]</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.003</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 0, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probability  cost                  solution  count\n",
       "35        0.009   3.0  [0, 0, 0, 0, 1, 0, 1, 1]      9\n",
       "14        0.016   3.0  [0, 0, 0, 0, 1, 1, 0, 1]     16\n",
       "6         0.023   3.0  [0, 1, 0, 0, 0, 0, 1, 1]     23\n",
       "1         0.035   3.0  [0, 0, 1, 1, 0, 0, 0, 1]     35\n",
       "92        0.003   3.0  [1, 1, 0, 1, 0, 0, 0, 0]      3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    mis_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687f492b-a4a5-49c6-964c-8959b035bb93",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.450643Z",
     "iopub.status.busy": "2024-05-07T15:52:25.449435Z",
     "iopub.status.idle": "2024-05-07T15:52:25.685563Z",
     "shell.execute_reply": "2024-05-07T15:52:25.684857Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0",
   "metadata": {},
   "source": [
    "Let us plot the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.690192Z",
     "iopub.status.busy": "2024-05-07T15:52:25.689045Z",
     "iopub.status.idle": "2024-05-07T15:52:25.694309Z",
     "shell.execute_reply": "2024-05-07T15:52:25.693659Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_solution = optimization_result.solution[optimization_result.cost.idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fed415f4-67ed-4a85-9138-553c75972ac8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.698802Z",
     "iopub.status.busy": "2024-05-07T15:52:25.697644Z",
     "iopub.status.idle": "2024-05-07T15:52:25.706511Z",
     "shell.execute_reply": "2024-05-07T15:52:25.705822Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Independent Set:  [2, 3, 7]\n",
      "Size of Independent Set:  3\n"
     ]
    }
   ],
   "source": [
    "independent_set = [node for node in graph.nodes if best_solution[node] == 1]\n",
    "print(\"Independent Set: \", independent_set)\n",
    "print(\"Size of Independent Set: \", len(independent_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9861107b-3b50-4272-a07c-c0c4566b13ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:25.711117Z",
     "iopub.status.busy": "2024-05-07T15:52:25.709952Z",
     "iopub.status.idle": "2024-05-07T15:52:26.053349Z",
     "shell.execute_reply": "2024-05-07T15:52:26.052556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw_kamada_kawai(graph, with_labels=True)\n",
    "nx.draw_kamada_kawai(\n",
    "    graph,\n",
    "    with_labels=True,\n",
    "    nodelist=independent_set,\n",
    "    node_color=\"r\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149932e1-bfa8-4c27-b5f9-037e74eba400",
   "metadata": {},
   "source": [
    "## Comparison to a classical solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde1905d-aeff-4297-a9d3-ad14910e6161",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:26.058419Z",
     "iopub.status.busy": "2024-05-07T15:52:26.057188Z",
     "iopub.status.idle": "2024-05-07T15:52:26.133076Z",
     "shell.execute_reply": "2024-05-07T15:52:26.132477Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(mis_model)\n",
    "classical_solution = [pyo.value(mis_model.x[i]) for i in graph.nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:26.137916Z",
     "iopub.status.busy": "2024-05-07T15:52:26.136534Z",
     "iopub.status.idle": "2024-05-07T15:52:26.144526Z",
     "shell.execute_reply": "2024-05-07T15:52:26.143929Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical Independent Set:  [0, 1, 3]\n",
      "Size of Classical Independent Set:  3\n"
     ]
    }
   ],
   "source": [
    "independent_set_classical = [\n",
    "    node for node in graph.nodes if np.allclose(classical_solution[node], 1)\n",
    "]\n",
    "print(\"Classical Independent Set: \", independent_set_classical)\n",
    "print(\"Size of Classical Independent Set: \", len(independent_set_classical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bc4df9d-771b-4df5-943b-9b142134e979",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:52:26.148888Z",
     "iopub.status.busy": "2024-05-07T15:52:26.147883Z",
     "iopub.status.idle": "2024-05-07T15:52:26.436437Z",
     "shell.execute_reply": "2024-05-07T15:52:26.435757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw_kamada_kawai(graph, with_labels=True)\n",
    "nx.draw_kamada_kawai(\n",
    "    graph,\n",
    "    with_labels=True,\n",
    "    nodelist=independent_set_classical,\n",
    "    node_color=\"r\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82b5953-122a-4707-8ab6-f741f14f13a5",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='MISWiki'>[1]</a>: [Max Independent Set (Wikipedia)](https://en.wikipedia.org/wiki/Partition_problem)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
